The Repository for Model-Driven Development (ReMoDD) contains artifacts that support Model-Driven Development (MDD) research and education. ReMoDD is collecting (1) documented MDD case studies, (2) examples of models reflecting good and bad modeling practices, (3) reference models (including metamodels) that can be used as the basis for comparing and evaluating MDD techniques, (4) generic models and transformations reflecting reusable modeling experience, (5) descriptions of modeling techniques, practices and experiences, and (6) modeling exercises and problems that can be used to develop classroom assignments and projects. ReMoDD provides a single point of access to shared artifacts reflecting high-quality MDD experience and knowledge from industry and academia. This access facilitates sharing of relevant knowledge and experience that improve MDD activities in research, education and industry.

A dynamically adaptive system (DAS) monitors itself and its execution environment to evaluate requirements satisfaction at run time. Unanticipated environmental conditions may produce sensory inputs that alter the self-assessment capabilities of a DAS in unpredictable and undesirable ways. Moreover, it is impossible for a human to know or enumerate all possible combinations of system and environmental conditions that a DAS may encounter throughout its lifetime. This paper introduces Loki, an approach for automatically discovering combinations of environmental conditions that produce requirements violations and latent behaviors in a DAS. By anticipating adverse environmental conditions that might arise at run time, Loki facilitates the identification of goals with inadequate obstacle mitigations or insufficient constraints to prevent such unwanted behaviors. We apply Loki to an autonomous vehicle system and describe several undesirable behaviors discovered.

Maintaining an object-oriented design for a piece of software is a difficult, time-consuming task. Prior approaches to automated design refactoring have focused on making small, iterative changes to a given software design. However, such approaches do not take advantage of composition of design changes, thus limiting the richness of the refactoring strategies that they can generate. In order to address this problem, this paper introduces an approach that supports composition of design changes and makes the introduction of design patterns a primary goal of the refactoring process. The proposed approach uses genetic programming and software engineering metrics to identify the most suitable set of refactorings to apply to a software design. We illustrate the efficacy of this approach by applying it to a large set of published models, as well as a real-world case study